Article thumbnail

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

By Vlad Popovici, Weijie Chen, Brandon G Gallas, Christos Hatzis, Weiwei Shi, Frank W Samuelson, Yuri Nikolsky, Marina Tsyganova, Alex Ishkin, Tatiana Nikolskaya, Kenneth R Hess, Vicente Valero, Daniel Booser, Mauro Delorenzi, Gabriel N Hortobagyi, Leming Shi, W Fraser Symmans and Lajos Pusztai
Topics: Research article
Publisher: BioMed Central
OAI identifier: oai:pubmedcentral.nih.gov:2880423
Provided by: PubMed Central

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles

Citations

  1. (2002). A gene-expression signature as a predictor of survival in breast cancer.
  2. (2005). Comparison of the predictive accuracy of DNA array-based multigene classifiers across cDNA arrays and Affymetrix genechips.
  3. (2002). Complexity measures of supervised classification problems.
  4. (2005). E: Outcome signature genes in breast cancer: is there a unique set?. Bioinformatics
  5. (2008). EA: Comparing the characteristics of gene expression profiles derived by univariate and multivariate classification methods. Stat Appl Genet Mol Biol
  6. (2004). Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer.
  7. (1989). Hayes RR: Effects of sample size in classifier design.
  8. (2008). Hortobágyi GN: Commercialized multigene predictors of clinical outcome for breast cancer. Oncologist
  9. (2009). Jurisica I: Prognostic gene signatures for non-small-cell lung cancer. Proc Natl Acad Sci USA
  10. (2006). L: Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer.
  11. (2006). LFA: A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets.
  12. (2007). Mengersen KL: Classification based upon gene expression data: bias and precision of error rates. Bioinformatics
  13. (2008). Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res
  14. (2006). MH: Assessing classifiers from two independent data sets using ROC analysis: a nonparametric approach.
  15. (2005). MH: Estimating the uncertainty in the estimated mean area under the ROC curve of a classifier. Pattern Recog Lett
  16. (2000). Molecular portraits of human breast tumours. Nature
  17. (2006). Perou CM: Concordance among gene-expression-based predictors for breast cancer.
  18. (2005). Pusztai L: Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res
  19. (2008). Pusztai L: HER2 expression and efficacy of preoperative paclitaxel/FAC chemotherapy in breast cancer. Breast Cancer Res Treat
  20. (2007). Pusztai L: Thirty-gene pharmacogenomic test correlates with residual cancer burden after preoperative chemotherapy for breast cancer. Clin Cancer Res
  21. (2007). RA: The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol
  22. (2008). Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer.
  23. (1997). Tibshirani R: Improvements on cross-validation: the 632+ bootstrap method.
  24. (2003). Total RNA yield and microarray gene expression profiles from fine-needle aspiration biopsy and core-needle biopsy samples of breast carcinoma. Cancer
  25. (2002). TP: Comparison of discrimination methods for the classification of tumors using gene expression data.
  26. (2004). Wolmark N: A multigene assay to predict recurrence of tamoxifen-treated, nodenegative breast cancer.